Joint audio de-noise and de-reverberation for videoconferencing

ABSTRACT

One disclosed example method includes a device receiving an audio signal recorded in a physical environment and applying a de-noise and de-reverberation model onto the audio signal to generate a cleaned audio signal. The de-noise and de-reverberation model is configured to remove noise and reverberation from the audio signal and is trained via a training process. The training process includes training the de-noise and de-reverberation model based on a trained de-noise teacher model and a trained de-reverberation teacher model. The training includes adjusting a portion of parameters of the de-noise and de-reverberation model based on values generated by the de-noise teacher model and the de-reverberation teacher model and then adjusting the parameters of the de-noise and de-reverberation model independently of the de-noise teacher model and the de-reverberation teacher model.

FIELD

The present application generally relates to videoconferences and moreparticularly relates to systems and methods for joint de-noise andde-reverberation of audio signals for videoconferences.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated into and constitute apart of this specification, illustrate one or more certain examples and,together with the description of the example, serve to explain theprinciples and implementations of certain examples.

FIG. 1 shows an example system that provides videoconferencingfunctionality to various client devices, according to certain aspectsdescribed herein.

FIG. 2 shows an example system in which a video conference providerprovides videoconferencing functionality to various client devices,according to certain aspects described herein.

FIG. 3A shows an example of an operating environment for joint de-noiseand de-reverberation of audio signals in videoconferences, according tocertain aspects described herein.

FIG. 3B shows an example of a flow chart that illustrates a process forgenerating a cleaned audio signal using a de-noise and de-reverberationmodel, according to certain aspects described herein.

FIG. 4 shows an example of a system configured for building and trainingvarious models involved in the training of a de-noise andde-reverberation model, according to certain aspects described herein.

FIG. 5 shows an example of a flow chart that illustrates a process fortraining a de-noise and de-reverberation model, according to certainaspects described herein.

FIG. 6 shows an example of the parameters of the models involved in thetraining of the de-noise and de-reverberation model, according tocertain aspects described herein.

FIG. 7 shows an example computing device suitable for implementingaspects of the techniques and technologies described herein.

DETAILED DESCRIPTION OF THE DRAWINGS

Examples are described herein in the context of systems and methods forjoint de-noise and de-reverberation of audio signals invideoconferences. Those of ordinary skill in the art will realize thatthe following description is illustrative only and is not intended to bein any way limiting. Reference will now be made in detail toimplementations of examples as illustrated in the accompanying drawings.The same reference indicators will be used throughout the drawings andthe following description to refer to the same or like items.

In the interest of clarity, not all of the routine features of theexamples described herein are shown and described. It will, of course,be appreciated that in the development of any such actualimplementation, numerous implementation-specific decisions must be madein order to achieve the developer's specific goals, such as compliancewith application- and business-related constraints, and that thesespecific goals will vary from one implementation to another and from onedeveloper to another.

Videoconferencing systems enable their users to create and attendvideoconferences (or “meetings”) via various types of client devices.After joining a meeting, the participants receive audio and videostreams or feeds (or “multimedia” streams or feeds) from the otherparticipants and are presented with views of the video feeds from one ormore of the other participants and audio from the audio feeds. Usingthese different modalities, the participants can see and hear eachother, engage more deeply, and generally have a richer experiencedespite not being physically in the same space.

However, when audio signals are captured at respective client devices,different distortions may be introduced. One distortion is the noisethat is captured along with the audio signal, which may be thebackground noise of the environment where the device is located, noisegenerated by inadvertent actions taken by the participant near the audiorecording device, or a defect of the audio recording device. Anotherdistortion is the reverberation effect of the audio signal captured bythe audio recording device. Reverberation is the persistence of soundafter the sound is produced. A reverberation is created when a sound orsignal is reflected causing numerous reflections to build up and thendecay as the sound is absorbed by the surfaces of objects in thespace—which could include furniture, people, and air. These distortionsprevent the captured audio from being heard clearly by otherparticipants of the meeting.

To provide high-quality audio signals, a videoconferencing systemaccording to this disclosure applies a de-noise and de-reverberationmodel to the captured audio signal to simultaneously remove the noiseand reverberation from the captured audio signal. In one example, aclient device captures the sound in the environment where the clientdevice is located. The client device further feeds the audio signal ofthe captured sound to a de-noise and de-reverberation model that isconfigured to simultaneously remove the noise and reverberation from theinput audio signal. The output of the de-noise and de-reverberationmodel is a cleaned audio signal. The client device can send the cleanedaudio signal along with other data associated with the meeting to otherparticipants. In some examples, the client device may use the cleanedaudio signal for other purposes, such as performing post-processing(e.g., speech recognition) on the cleaned audio signal.

The de-noise and de-reverberation model can be obtained via guidedtraining using two auxiliary models, also referred to herein as “teachermodels.” One teacher model is configured to remove noise from inputaudio signals (“de-noise teacher model”) and the other teacher model isconfigured to remove reverberation from input audio signals(“de-reverberation teacher model”). Compared with the two teachermodels, the de-noise and de-reverberation model has a less complicatedstructure and thus requires fewer computations to operate. For example,if the de-noise and de-reverberation model and the two teacher modelsare neural network models, the de-noise and de-reverberation model canbe configured with fewer layers and fewer nodes than each of the teachermodels.

To train the de-noise and de-reverberation model, a model trainingsystem can train the two teacher models first. A training dataset isgenerated for each of the teacher models. For example, for the de-noiseteacher model, a training dataset containing noisy audio signals can begenerated. The noisy audio signals can be generated by adding noises ofdifferent types with different strengths to clean audio signals. For thede-reverberation teacher model, a training dataset containingreverberated samples or audio signals can be generated. The reverberatedsamples or audio signals can be generated by adding reverberations ofdifferent types with different strengths to clean audio signals. Thede-noise teacher model and the de-reverberation teacher model can befully trained using the respective training datasets.

The training of the de-noise and de-reverberation model includes twostages. In the first stage, values generated by the de-noise teachermodel and de-reverberation teacher model are utilized to guide thetraining of a portion of the parameters of the de-noise andde-reverberation model. In the example where the models are neuralnetwork models, the model training system can retrieve the output valuesof a hidden layer (e.g., the last hidden layer) from each of the twoteacher models. The model training system can further adjust theparameters of the de-noise and de-reverberation model in the input layerand the hidden layers to minimize a loss function defined based on thehidden layer output values of the two teacher models.

The second stage of the training is performed independently of the twoteacher models. In this stage, the parameters of the entire de-noise andde-reverberation model are adjusted or updated to minimize a lossfunction defined based on the similarity between the cleaned audiosignals by the model and the ground truth clean audio signal in thetraining dataset for the de-noise and de-reverberation model. Thetrained de-noise and de-reverberation model can be provided to clientdevices of the videoconferencing system to simultaneously remove noiseand reverberation from the recorded audio signals as described above.

The techniques disclosed herein for joint de-noise and de-reverberationof audio signals in videoconferences improve the audio quality of thevideoconferencing. By removing the noise and reverberation from theaudio signals recorded at individual client devices, high-quality audiosignals can be delivered to other participants of the meeting. Further,compared with approaches where the noise and reverberation are removedin separate steps using separate models, the joint removal of noise andreverberation using one model can significantly reduce the computationalcomplexity of the audio cleaning process and the memory space used tostore the model. Yet, the audio quality of the cleaned audio signals ismaintained high because of the guided training from the more complexteacher models. As a result, the de-noise and de-reverberation model canhave similar audio quality in the cleaned audio signal as the twoteacher models but with a much lower computational complexity and lesscomplicated model structure.

This illustrative example is given to introduce the reader to thegeneral subject matter discussed herein and the disclosure is notlimited to this example. The following sections describe variousadditional non-limiting examples and examples of systems and methods forjoint de-noise and de-reverberation of audio signals forvideoconferences.

Referring now to FIG. 1 , FIG. 1 shows an example system 100 thatprovides videoconferencing functionality to various client devices. Thesystem 100 includes a video conference provider 110 that is connected tomultiple communication networks 120, 130, through which various clientdevices 140-180 can participate in video conferences hosted by the videoconference provider 110. For example, the video conference provider 110can be located within a private network to provide video conferencingservices to devices within the private network, or it can be connectedto a public network, e.g., the internet, so it may be accessed byanyone. Some examples may even provide a hybrid model in which a videoconference provider 110 may supply components to enable a privateorganization to host private internal video conferences or to connectits system to the video conference provider 110 over a public network.

The system optionally also includes one or more user identity providers,e.g., user identity provider 115, which can provide user identityservices to users of the client devices 140-160 and may authenticateuser identities of one or more users to the video conference provider110. In this example, the user identity provider 115 is operated by adifferent entity than the video conference provider 110, though in someexamples, they may be the same entity.

Video conference provider 110 allows clients to create videoconferencemeetings (or “meetings”) and invite others to participate in thosemeetings as well as perform other related functionality, such asrecording the meetings, generating transcripts from meeting audio,manage user functionality in the meetings, enable text messaging duringthe meetings, create and manage breakout rooms from the main meeting,etc. FIG. 2 , described below, provides a more detailed description ofthe architecture and functionality of the video conference provider 110.

Meetings in this example video conference provider 110 are provided invirtual “rooms” to which participants are connected. The room in thiscontext is a construct provided by a server that provides a common pointat which the various video and audio data is received before beingmultiplexed and provided to the various participants. While a “room” isthe label for this concept in this disclosure, any suitablefunctionality that enables multiple participants to participate in acommon videoconference may be used. Further, in some examples, and asalluded to above, a meeting may also have “breakout” rooms. Suchbreakout rooms may also be rooms that are associated with a “main”videoconference room. Thus, participants in the main videoconferenceroom may exit the room into a breakout room, e.g., to discuss aparticular topic, before returning to the main room. The breakout roomsin this example are discrete meetings that are associated with themeeting in the main room. However, to join a breakout room, aparticipant must first enter the main room. A room may have any numberof associated breakout rooms according to various examples.

To create a meeting with the video conference provider 110, a user maycontact the video conference provider 110 using a client device 140-180and select an option to create a new meeting. Such an option may beprovided in a webpage accessed by a client device 140-160 or a clientapplication executed by a client device 140-160. For telephony devices,the user may be presented with an audio menu that they may navigate bypressing numeric buttons on their telephony device. To create themeeting, the video conference provider 110 may prompt the user forcertain information, such as a date, time, and duration for the meeting,a number of participants, a type of encryption to use, whether themeeting is confidential or open to the public, etc. After receiving thevarious meeting settings, the video conference provider may create arecord for the meeting and generate a meeting identifier and, in someexamples, a corresponding meeting password or passcode (or otherauthentication information), all of which meeting information isprovided to the meeting host.

After receiving the meeting information, the user may distribute themeeting information to one or more users to invite them to the meeting.To begin the meeting at the scheduled time (or immediately, if themeeting was set for an immediate start), the host provides the meetingidentifier and, if applicable, corresponding authentication information(e.g., a password or passcode). The video conference system theninitiates the meeting and may admit users to the meeting. Depending onthe options set for the meeting, the users may be admitted immediatelyupon providing the appropriate meeting identifier (and authenticationinformation, as appropriate), even if the host has not yet arrived, orthe users may be presented with information indicating the that meetinghas not yet started or the host may be required to specifically admitone or more of the users.

During the meeting, the participants may employ their client devices140-180 to capture audio or video information and stream thatinformation to the video conference provider 110. They also receiveaudio or video information from the video conference provider 210, whichis displayed by the respective client device 140 to enable the varioususers to participate in the meeting.

At the end of the meeting, the host may select an option to terminatethe meeting, or it may terminate automatically at a scheduled end timeor after a predetermined duration. When the meeting terminates, thevarious participants are disconnected from the meeting and they will nolonger receive audio or video streams for the meeting (and will stoptransmitting audio or video streams). The video conference provider 110may also invalidate the meeting information, such as the meetingidentifier or password/passcode.

To provide such functionality, one or more client devices 140-180 maycommunicate with the video conference provider 110 using one or morecommunication networks, such as network 120 or the public switchedtelephone network (“PSTN”) 130. The client devices 140-180 may be anysuitable computing or communications device that has audio or videocapability. For example, client devices 140-160 may be conventionalcomputing devices, such as desktop or laptop computers having processorsand computer-readable media, connected to the video conference provider110 using the internet or other suitable computer network. Suitablenetworks include the internet, any local area network (“LAN”), metroarea network (“MAN”), wide area network (“WAN”), cellular network (e.g.,3G, 4G, 4G LTE, 5G, etc.), or any combination of these. Other types ofcomputing devices may be used instead or as well, such as tablets,smartphones, and dedicated video conferencing equipment. Each of thesedevices may provide both audio and video capabilities and may enable oneor more users to participate in a video conference meeting hosted by thevideo conference provider 110.

In addition to the computing devices discussed above, client devices140-180 may also include one or more telephony devices, such as cellulartelephones (e.g., cellular telephone 170), internet protocol (“IP”)phones (e.g., telephone 180), or conventional telephones. Such telephonydevices may allow a user to make conventional telephone calls to othertelephony devices using the PSTN, including the video conferenceprovider 110. It should be appreciated that certain computing devicesmay also provide telephony functionality and may operate as telephonydevices. For example, smartphones typically provide cellular telephonecapabilities and thus may operate as telephony devices in the examplesystem 100 shown in FIG. 1 . In addition, conventional computing devicesmay execute software to enable telephony functionality, which may allowthe user to make and receive phone calls, e.g., using a headset andmicrophone. Such software may communicate with a PSTN gateway to routethe call from a computer network to the PSTN. Thus, telephony devicesencompass any devices that can make conventional telephone calls and arenot limited solely to dedicated telephony devices like conventionaltelephones.

Referring again to client devices 140-160, these devices 140-160 contactthe video conference provider 110 using network 120 and may provideinformation to the video conference provider 110 to access functionalityprovided by the video conference provider 110, such as access to createnew meetings or join existing meetings. To do so, the client devices140-160 may provide user identification information, meetingidentifiers, meeting passwords or passcodes, etc. In examples thatemploy a user identity provider 115, a client device, e.g., clientdevices 140-160, may operate in conjunction with a user identityprovider 115 to provide user identification information or other userinformation to the video conference provider 110.

A user identity provider 115 may be any entity trusted by the videoconference provider 110 that can help identify a user to the videoconference provider 110. For example, a trusted entity may be a serveroperated by a business or other organization and with whom the user hasestablished their identity, such as an employer or trusted third party.The user may sign into the user identity provider 115, such as byproviding a username and password, to access their identity at the useridentity provider 115. The identity, in this sense, is informationestablished and maintained at the user identity provider 115 that can beused to identify a particular user, irrespective of the client devicethey may be using. An example of an identity may be an email accountestablished at the user identity provider 115 by the user and secured bya password or additional security features, such as biometricauthentication, two-factor authentication, etc. However, identities maybe distinct from functionality such as email. For example, a health careprovider may establish identities for its patients. And while suchidentities may have associated email accounts, the identity is distinctfrom those email accounts. Thus, a user's “identity” relates to asecure, verified set of information that is tied to a particular userand should be accessible only by that user. By accessing the identity,the associated user may then verify themselves to other computingdevices or services, such as the video conference provider 110.

When the user accesses the video conference provider 110 using a clientdevice, the video conference provider 110 communicates with the useridentity provider 115 using information provided by the user to verifythe user's identity. For example, the user may provide a username orcryptographic signature associated with a user identity provider 115.The user identity provider 115 then either confirms the user's identityor denies the request. Based on this response, the video conferenceprovider 110 either provides or denies access to its services,respectively.

For telephony devices, e.g., client devices 170-180, the user may placea telephone call to the video conference provider 110 to access videoconference services. After the call is answered, the user may provideinformation regarding a video conference meeting, e.g., a meetingidentifier (“ID”), a passcode or password, etc., to allow the telephonydevice to join the meeting and participate using audio devices of thetelephony device, e.g., microphone(s) and speaker(s), even if videocapabilities are not provided by the telephony device.

Because telephony devices typically have more limited functionality thanconventional computing devices, they may be unable to provide certaininformation to the video conference provider 110. For example, telephonydevices may be unable to provide user identification information toidentify the telephony device or the user to the video conferenceprovider 110. Thus, the video conference provider 110 may provide morelimited functionality to such telephony devices. For example, the usermay be permitted to join a meeting after providing meeting information,e.g., a meeting identifier and passcode, but they may be identified onlyas an anonymous participant in the meeting. This may restrict theirability to interact with the meetings in some examples, such as bylimiting their ability to speak in the meeting, hear or view certaincontent shared during the meeting, or access other meetingfunctionality, such as joining breakout rooms or engaging in text chatwith other participants in the meeting.

It should be appreciated that users may choose to participate inmeetings anonymously and decline to provide user identificationinformation to the video conference provider 110, even in cases wherethe user has an authenticated identity and employs a client devicecapable of identifying the user to the video conference provider 110.The video conference provider 110 may determine whether to allow suchanonymous users to use services provided by the video conferenceprovider 110. Anonymous users, regardless of the reason for anonymity,may be restricted as discussed above with respect to users employingtelephony devices, and in some cases may be prevented from accessingcertain meetings or other services, or may be entirely prevented fromaccessing the video conference provider.

Referring again to video conference provider 110, in some examples, itmay allow client devices 140-160 to encrypt their respective video andaudio streams to help improve privacy in their meetings. Encryption maybe provided between the client devices 140-160 and the video conferenceprovider 110 or it may be provided in an end-to-end configuration wheremultimedia streams transmitted by the client devices 140-160 are notdecrypted until they are received by another client device 140-160participating in the meeting. Encryption may also be provided duringonly a portion of a communication, for example encryption may be usedfor otherwise unencrypted communications that cross internationalborders.

Client-to-server encryption may be used to secure the communicationsbetween the client devices 140-160 and the video conference provider110, while allowing the video conference provider 110 to access thedecrypted multimedia streams to perform certain processing, such asrecording the meeting for the participants or generating transcripts ofthe meeting for the participants. End-to-end encryption may be used tokeep the meeting entirely private to the participants without any worryabout a video conference provider 110 having access to the substance ofthe meeting. Any suitable encryption methodology may be employed,including key-pair encryption of the streams. For example, to provideend-to-end encryption, the meeting host's client device may obtainpublic keys for each of the other client devices participating in themeeting and securely exchange a set of keys to encrypt and decryptmultimedia content transmitted during the meeting. Thus the clientdevices 140-160 may securely communicate with each other during themeeting. Further, in some examples, certain types of encryption may belimited by the types of devices participating in the meeting. Forexample, telephony devices may lack the ability to encrypt and decryptmultimedia streams. Thus, while encrypting the multimedia streams may bedesirable in many instances, it is not required as it may prevent someusers from participating in a meeting.

By using the example system shown in FIG. 1 , users can create andparticipate in meetings using their respective client devices 140-180via the video conference provider 110. Further, such a system enablesusers to use a wide variety of different client devices 140-180 fromtraditional standards-based video conferencing hardware to dedicatedvideo conferencing equipment to laptop or desktop computers to handhelddevices to legacy telephony devices. etc.

Referring now to FIG. 2 , FIG. 2 shows an example system 200 in which avideo conference provider 210 provides videoconferencing functionalityto various client devices 220-250. The client devices 220-250 includetwo conventional computing devices 220-230, dedicated equipment for avideo conference room 240, and a telephony device 250. Each clientdevice 220-250 communicates with the video conference provider 210 overa communications network, such as the internet for client devices220-240 or the PSTN for client device 250, generally as described abovewith respect to FIG. 1 . The video conference provider 210 is also incommunication with one or more user identity providers 215, which canauthenticate various users to the video conference provider 210generally as described above with respect to FIG. 1 .

In this example, the video conference provider 210 employs multipledifferent servers (or groups of servers) to provide different aspects ofvideo conference functionality, thereby enabling the various clientdevices to create and participate in video conference meetings. Thevideo conference provider 210 uses one or more real-time media servers212, one or more network services servers 214, one or more video roomgateways 216, and one or more telephony gateways 218. Each of theseservers 212-218 is connected to one or more communications networks toenable them to collectively provide access to and participation in oneor more video conference meetings to the client devices 220-250.

The real-time media servers 212 provide multiplexed multimedia streamsto meeting participants, such as the client devices 220-250 shown inFIG. 2 . While video and audio streams typically originate at therespective client devices, they are transmitted from the client devices220-250 to the video conference provider 210 via one or more networkswhere they are received by the real-time media servers 212. Thereal-time media servers 212 determine which protocol is optimal basedon, for example, proxy settings and the presence of firewalls, etc. Forexample, the client device might select among UDP, TCP, TLS, or HTTPSfor audio and video and UDP for content screen sharing.

The real-time media servers 212 then multiplex the various video andaudio streams based on the target client device and communicatemultiplexed streams to each client device. For example, the real-timemedia servers 212 receive audio and video streams from client devices220-240 and only an audio stream from client device 250. The real-timemedia servers 212 then multiplex the streams received from devices230-250 and provide the multiplexed stream to client device 220. Thereal-time media servers 212 are adaptive, for example, reacting toreal-time network and client changes, in how they provide these streams.For example, the real-time media servers 212 may monitor parameters suchas a client's bandwidth CPU usage, memory, and network I/O as well asnetwork parameters such as packet loss, latency, and jitter to determinehow to modify the way in which streams are provided.

The client device 220 receives the stream, performs any decryption,decoding, and demultiplexing on the received streams, and then outputsthe audio and video using the client device's video and audio devices.In this example, the real-time media servers do not multiplex clientdevice 220's own video and audio feeds when transmitting streams to it.Instead, each client device 220-250 only receives multimedia streamsfrom other client devices 220-250. For telephony devices that lack videocapabilities, e.g., client device 250, the real-time media servers 212only deliver multiplex audio streams. The client device 220 may receivemultiple streams for a particular communication, allowing the clientdevice 220 to switch between streams to provide a higher quality ofservice.

In addition to multiplexing multimedia streams, the real-time mediaservers 212 may also decrypt incoming multimedia streams in someexamples. As discussed above, multimedia streams may be encryptedbetween the client devices 220-250 and the video conference provider210. In some such examples, the real-time media servers 212 may decryptincoming multimedia streams, multiplex the multimedia streamsappropriately for the various clients, and encrypt the multiplexedstreams for transmission.

In some examples, to provide multiplexed streams, the video conferenceprovider 210 may receive multimedia streams from the variousparticipants and publish those streams to the various participants tosubscribe to and receive. Thus, the video conference provider 210notifies a client device, e.g., client device 220, about variousmultimedia streams available from the other client devices 230-250, andthe client device 220 can select which multimedia stream(s) to subscribeto and receive. In some examples, the video conference provider 210 mayprovide to each client device the available streams from the otherclient devices, but from the respective client device itself, though inother examples it may provide all available streams to all availableclient devices. Using such a multiplexing technique, the videoconference provider 210 may enable multiple different streams of varyingquality, thereby allowing client devices to change streams in real-timeas needed, e.g., based on network bandwidth, latency, etc.

As mentioned above with respect to FIG. 1 , the video conferenceprovider 210 may provide certain functionality with respect tounencrypted multimedia streams at a user's request. For example, themeeting host may be able to request that the meeting be recorded or thata transcript of the audio streams be prepared, which may then beperformed by the real-time media servers 212 using the decryptedmultimedia streams, or the recording or transcription functionality maybe off-loaded to a dedicated server (or servers), e.g., cloud recordingservers, for recording the audio and video streams. In some examples,the video conference provider 210 may allow a meeting participant tonotify it of inappropriate behavior or content in a meeting. Such anotification may trigger the real-time media servers to 212 record aportion of the meeting for review by the video conference provider 210.Still other functionality may be implemented to take actions based onthe decrypted multimedia streams at the video conference provider 210,such as monitoring video or audio quality, adjusting or changing mediaencoding mechanisms, etc.

It should be appreciated that multiple real-time media servers 212 maybe involved in communicating data for a single meeting and multimediastreams may be routed through multiple different real-time media servers212. In addition, the various real-time media servers 212 may not beco-located, but instead may be located at multiple different geographiclocations, which may enable high-quality communications between clientsthat are dispersed over wide geographic areas, such as being located indifferent countries or on different continents. Further, in someexamples, one or more of these servers may be co-located on a client'spremises, e.g., at a business or other organization. For example,different geographic regions may each have one or more real-time mediaservers 212 to enable client devices in the same geographic region tohave a high-quality connection into the video conference provider 210via local servers 212 to send and receive multimedia streams, ratherthan connecting to a real-time media server located in a differentcountry or on a different continent. The local real-time media servers212 may then communicate with physically distant servers usinghigh-speed network infrastructure, e.g., internet backbone network(s),that otherwise might not be directly available to client devices 220-250themselves. Thus, routing multimedia streams may be distributedthroughout the video conference system 210 and across many differentreal-time media servers 212.

Turning to the network services servers 214, these servers 214 provideadministrative functionality to enable client devices to create orparticipate in meetings, send meeting invitations, create or manage useraccounts or subscriptions, and other related functionality. Further,these servers may be configured to perform different functionalities orto operate at different levels of a hierarchy, e.g., for specificregions or localities, to manage portions of the video conferenceprovider under a supervisory set of servers. When a client device220-250 accesses the video conference provider 210, it will typicallycommunicate with one or more network services servers 214 to accesstheir account or to participate in a meeting.

When a client device 220-250 first contacts the video conferenceprovider 210 in this example, it is routed to a network services server214. The client device may then provide access credentials for a user,e.g., a username and password or single sign-on credentials, to gainauthenticated access to the video conference provider 210. This processmay involve the network services servers 214 contacting a user identityprovider 215 to verify the provided credentials. Once the user'scredentials have been accepted, the client device may performadministrative functionality, like updating user account information, ifthe user has an identity with the video conference provider 210, orscheduling a new meeting, by interacting with the network servicesservers 214.

In some examples, users may access the video conference provider 210anonymously. When communicating anonymously, a client device 220-250 maycommunicate with one or more network services servers 214 but onlyprovide information to create or join a meeting, depending on whatfeatures the video conference provider allows for anonymous users. Forexample, an anonymous user may access the video conference providerusing client device 220 and provide a meeting ID and passcode. Thenetwork services server 214 may use the meeting ID to identify anupcoming or on-going meeting and verify the passcode is correct for themeeting ID. After doing so, the network services server(s) 214 may thencommunicate information to the client device 220 to enable the clientdevice 220 to join the meeting and communicate with appropriatereal-time media servers 212.

In cases where a user wishes to schedule a meeting, the user (anonymousor authenticated) may select an option to schedule a new meeting and maythen select various meeting options, such as the date and time for themeeting, the duration for the meeting, a type of encryption to be used,one or more users to invite, privacy controls (e.g., not allowinganonymous users, preventing screen sharing, manually authorize admissionto the meeting, etc.), meeting recording options, etc. The networkservices servers 214 may then create and store a meeting record for thescheduled meeting. When the scheduled meeting time arrives (or within athreshold period of time in advance), the network services server(s) 214may accept requests to join the meeting from various users.

To handle requests to join a meeting, the network services server(s) 214may receive meeting information, such as a meeting ID and passcode, fromone or more client devices 220-250. The network services server(s) 214locate a meeting record corresponding to the provided meeting ID andthen confirm whether the scheduled start time for the meeting hasarrived, whether the meeting host has started the meeting, and whetherthe passcode matches the passcode in the meeting record. If the requestis made by the host, the network services server(s) 214 activates themeeting and connects the host to a real-time media server 212 to enablethe host to begin sending and receiving multimedia streams.

Once the host has started the meeting, subsequent users requestingaccess will be admitted to the meeting if the meeting record is locatedand the passcode matches the passcode supplied by the requesting clientdevice 220-250. In some examples, additional access controls may be usedas well. But if the network services server(s) 214 determines to admitthe requesting client device 220-250 to the meeting, the networkservices server 214 identifies a real-time media server 212 to handlemultimedia streams to and from the requesting client device 220-250 andprovides information to the client device 220-250 to connect to theidentified real-time media server 212. Additional client devices 220-250may be added to the meeting as they request access through the networkservices server(s) 214.

After joining a meeting, client devices will send and receive multimediastreams via the real-time media servers 212, but they may alsocommunicate with the network services servers 214 as needed duringmeetings. For example, if the meeting host leaves the meeting, thenetwork services server(s) 214 may appoint another user as the newmeeting host and assign host administrative privileges to that user.Hosts may have administrative privileges to allow them to manage theirmeetings, such as by enabling or disabling screen sharing, muting orremoving users from the meeting, creating sub-meetings or “break-out”rooms, recording meetings, etc. Such functionality may be managed by thenetwork services server(s) 214.

For example, if a host wishes to remove a user from a meeting, they mayidentify the user and issue a command through a user interface on theirclient device. The command may be sent to a network services server 214,which may then disconnect the identified user from the correspondingreal-time media server 212. If the host wishes to create a break-outroom for one or more meeting participants to join, such a command mayalso be handled by a network services server 214, which may create a newmeeting record corresponding to the break-out room and then connect oneor more meeting participants to the break-out room similarly to how itoriginally admitted the participants to the meeting itself.

In addition to creating and administering on-going meetings, the networkservices server(s) 214 may also be responsible for closing andtearing-down meetings once they have completed. For example, the meetinghost may issue a command to end an on-going meeting, which is sent to anetwork services server 214. The network services server 214 may thenremove any remaining participants from the meeting, communicate with oneor more real time media servers 212 to stop streaming audio and videofor the meeting, and deactivate, e.g., by deleting a correspondingpasscode for the meeting from the meeting record, or delete the meetingrecord(s) corresponding to the meeting. Thus, if a user later attemptsto access the meeting, the network services server(s) 214 may deny therequest.

Depending on the functionality provided by the video conferenceprovider, the network services server(s) 214 may provide additionalfunctionality, such as by providing private meeting capabilities fororganizations, special types of meetings (e.g., webinars), etc. Suchfunctionality may be provided according to various examples of videoconferencing providers according to this description.

Referring now to the video room gateway servers 216, these servers 216provide an interface between dedicated video conferencing hardware, suchas may be used in dedicated video conferencing rooms. Such videoconferencing hardware may include one or more cameras and microphonesand a computing device designed to receive video and audio streams fromeach of the cameras and microphones and connect with the videoconference provider 210. For example, the video conferencing hardwaremay be provided by the video conference provider to one or more of itssubscribers, which may provide access credentials to the videoconferencing hardware to use to connect to the video conferenceprovider.

The video room gateway servers 216 provide specialized authenticationand communication with the dedicated video conferencing hardware thatmay not be available to other client devices 220-230, 250. For example,the video conferencing hardware may register with the video conferenceprovider when it is first installed and the video room gateway mayauthenticate the video conferencing hardware using such registration aswell as information provided to the video room gateway server(s) 216when dedicated video conferencing hardware connects to it, such asdevice ID information, subscriber information, hardware capabilities,hardware version information, etc. Upon receiving such information andauthenticating the dedicated video conferencing hardware, the video roomgateway server(s) 216 may interact with the network services servers 214and real-time media servers 212 to allow the video conferencing hardwareto create or join meetings hosted by the video conference provider 210.

Referring now to the telephony gateway servers 218, these servers 218enable and facilitate telephony devices' participation in meetingshosted by the video conference provider. Because telephony devicescommunicate using the PSTN and not using computer networking protocols,such as TCP/IP, the telephony gateway servers 218 act as an interfacethat converts between the PSTN and the networking system used by thevideo conference provider 210.

For example, if a user uses a telephony device to connect to a meeting,they may dial a phone number corresponding to one of the videoconference provider's telephony gateway servers 218. The telephonygateway server 218 will answer the call and generate audio messagesrequesting information from the user, such as a meeting ID and passcode.The user may enter such information using buttons on the telephonydevice, e.g., by sending dual-tone multi-frequency (“DTMF”) audiosignals to the telephony gateway server 218. The telephony gatewayserver 218 determines the numbers or letters entered by the user andprovides the meeting ID and passcode information to the network servicesservers 214, along with a request to join or start the meeting,generally as described above. Once the telephony client device 250 hasbeen accepted into a meeting, the telephony gateway server 218 isinstead joined to the meeting on the telephony device's behalf.

After joining the meeting, the telephony gateway server 218 receives anaudio stream from the telephony device and provides it to thecorresponding real-time media server 212, and receives audio streamsfrom the real-time media server 212, decodes them, and provides thedecoded audio to the telephony device. Thus, the telephony gatewayservers 218 operate essentially as client devices, while the telephonydevice operates largely as an input/output device, e.g., a microphoneand speaker, for the corresponding telephony gateway server 218, therebyenabling the user of the telephony device to participate in the meetingdespite not using a computing device or video.

It should be appreciated that the components of the video conferenceprovider 210 discussed above are merely examples of such devices and anexample architecture. Some video conference providers may provide moreor less functionality than described above and may not separatefunctionality into different types of servers as discussed above.Instead, any suitable servers and network architectures may be usedaccording to different examples.

Referring now to FIG. 3A, FIG. 3A shows an example of an operatingenvironment 300A for joint de-noise and de-reverberation of audiosignals for videoconferences, according to certain aspects describedherein. The operating environment 300A includes the video conferenceprovider 210 as described above with respect to FIGS. 1 and 2 , andclient devices 304A-304N associated with participants of the meeting.The client devices 304A-304N may be referred to herein individually as aclient device 304 or collectively as the client devices 304. The clientdevices 304 may be any type of client device, such as those discussedabove with respect to FIGS. 1 and 2 .

As discussed above with respect to FIGS. 1 and 2 , the video conferenceprovider 210 is configured to provide video conference functionalitiesfor the client devices 304. During the meeting, a client device 304 maycapture an audio signal 306 in a physical environment where the clientdevice 304 is located through an audio recording device, as such amicrophone. The captured audio signal 306 may include the speech signalof the participant or other audio signal to be transmitted to the otherparticipants. Depending on the location where a participant joins themeeting using the client device 304, the physical environment may be aroom, an office, a car, an outdoor area, and so on.

When the audio signal 306 is being captured, different distortions maybe introduced. One distortion is the noise that is captured along withthe audio signal, which may be the background noise of the environment,noise generated by inadvertent operation of the participant near theaudio recording device, or a defect of the audio recording device.Another distortion is the reverberation effect of the audio signalcaptured by the audio recording device. Reverberation is the persistenceof a sound after the sound is produced. A reverberation is created whena sound or signal is reflected causing numerous reflections to build upand then decay as the sound is absorbed by the surfaces of objects inthe space—which could include furniture, people, and air.

To remove the noise and reverberation from the captured audio signal306, the client device 304 can employ a de-noise and de-reverberationmodel 312 that is configured to simultaneously remove the noise andreverberation from the captured audio signal 306. The de-noise andde-reverberation model 312 generates cleaned audio signal 308 which issent by the client device 304 to other client devices associated withother participants of the meeting through video conference provider 210.

While FIG. 3A shows that the client devices 304 use the de-noise andde-reverberation model 312 to clean up the captured audio signal 306before sending it to the video conference provider 210, otherarrangements are also possible. For example, the video conferenceprovider 210 can be configured with a de-noise and de-reverberationmodel, and each client device 304 can send the captured audio signal 306to the video conference provider 210. The video conference provider 210cleans up the received audio signals using the de-noise andde-reverberation model before sending the audio signals to otherparticipant client devices. In another example, some client devices havede-noise and de-reverberation models installed and some do not. Thoseclient devices that do not have the de-noise and de-reverberation model(e.g., a client device that has a lower version of the clientapplication for the meeting) can send the captured audio signal 306 tothe video conference provider 210 to clean up the audio signal. In thisexample, the data packets sent from a client device 304 to the videoconference provider 210 can include a flag indicating whether the audiosignal has been cleaned up or not. For the received audio signals thathave not been cleaned up, the video conference provider 210 can use thede-noise and de-reverberation model to generate the cleaned audio signalbefore sending it to other participants. For audio signals that havealready been cleaned up at the respective client devices, the videoconference provider 210 can forward them to other participants asdescribed above with respect to FIGS. 1 and 2 .

FIG. 3B shows an example of a flow chart that illustrates a process 300Bfor generating a cleaned audio signal using a de-noise andde-reverberation model, according to certain aspects described herein.FIG. 3B will be described with respect to the system shown in FIG. 3 .However, any suitable system according to this disclosure may beemployed. The client device 304 can implement the operations in theprocess 300B to clean up the captured audio signal before sending it tothe video conference provider 210. The video conference provider 210 canperform the process 300B to clean up the audio signal received from aclient device that participates in the meeting and has not or does nothave the capability to clean up the captured audio signal.

At block 320, the process 300B involves the client device 304 joining ameeting or the video conference provider 210 establish a meeting. Atblock 330, the process 300B involves receiving a captured audio signalthat is recorded in a physical environment. The client device 304receives the captured audio signal from an audio recording device, suchas a microphone associated with the client device 304. The videoconference provider 210 can receive the captured audio signal from aclient device that has joined the meeting and has not cleaned the audiosignal before transmitting it to the video conference provider 210.

At block 340, the process 300B involves the client device 304 or thevideo conference provider 210 applying a de-noise and de-reverberationmodel 312 onto the captured audio signal to generate a cleaned audiosignal as discussed above with respect to FIG. 3A. Based on theconfiguration of the de-noise and de-reverberation model, the clientdevice 304 or the video conference provider 210 can process the capturedaudio signal to transform it into a format that can be accepted by thede-noise and de-reverberation model. For example, the client device 304or the video conference provider 210 can divide the captured audiosignal into segments and apply a transformation on the segments totransform them into a frequency domain. Other processing may also beperformed to prepare the captured audio signal for input to the de-noiseand de-reverberation model. Similarly, the output of the de-noise andde-reverberation model may also be processed to generate the cleanedaudio signal. For example, if the direct output of the de-noise andde-reverberation model is audio segments in the frequency domain, aninverse transform can be applied to the segments transform them back tothe temporal domain. These inverse-transformed signals may beconcatenated together to generate the cleaned audio signal.

At block 350, the process involves outputting the cleaned audio signal.For example, the client device 304 may transmit the cleaned audio signalto the video conference provider 210 for transmission to otherparticipating client devices. Likewise, the video conference provider210 may also transmit the cleaned audio signal to other participatingclient devices. In some examples, outputting may also involve playingthe cleaned audio signal through an audio output device, such as thespeaker, or sending the cleaned audio signal to a component configuredto further process the cleaned audio signal to perform, for example,speech recognition or voice recognition.

The de-noise and de-reverberation model 312 may also be used inapplications other than videoconferencing. In those applications, block320 can be skipped and a computing device can employ block 330-350 tosimultaneously remove noise and reverberation from a captured audiosignal.

Referring now to FIG. 4 , FIG. 4 shows an example of a system configuredfor building and training various models involved in the training of ade-noise and de-reverberation model, according to certain aspectsdescribed herein. As shown in FIG. 4 , a model training system 408 isemployed to build and train three models: a de-noise teacher model 412,a de-reverberation teacher model 414, and a de-noise andde-reverberation model 312. The de-noise teacher model 412 is configuredto remove noise from input audio signals. The de-reverberation teachermodel 414 is configured to remove reverberation from input audiosignals. The de-noise and de-reverberation model 312 is configured toremove noise and reverberation simultaneously from the input audiosignals. In some examples, the de-noise teacher model 412, thede-reverberation teacher model 414, and the de-noise andde-reverberation model 312 are regression models, such as neural networkmodels.

Compared with the two teacher models, the de-noise and de-reverberationmodel 312 has a less complicated structure and thus requires fewercomputations to operate. In the example where all three models areneural network models, the de-noise and de-reverberation model 312 canbe configured with fewer layers and fewer nodes than each of the teachermodels. For instance, the number of layers in the de-noise andde-reverberation model 312 can be 30%-60% of the number of layers in thede-noise teacher model 412 or the de-reverberation teacher model 414.The number of nodes in the de-noise and de-reverberation model 312 canbe 20%-40% of the number of nodes in the de-noise teacher model 412 orthe de-reverberation teacher model 414. By setting up the three modelsin this way, the de-noise teacher model 412 and the de-reverberationteacher model 414 can be configured to utilize their respective complexmodel structures to capture the relationship between the input audiosignal and the output signal to efficiently clean up the input audiosignals. As discussed below, these two teacher models can be used toguide the training of the less complex de-noise and de-reverberationmodel so that the de-noise and de-reverberation model can obtain anaccurate output with a simpler model structure thereby requiring lesscomputational complexity.

To train the de-noise and de-reverberation model, a model trainingsystem can train the two teacher models first. A training dataset isgenerated for each of the teacher models. For example, for the de-noiseteacher model 412, a de-noise training dataset 402 containing noisyaudio signals or samples can be generated. The noisy audio signals canbe generated by adding noises of different types with differentstrengths to clean audio signals. For the de-reverberation teacher model414, a de-reverberation training dataset 404 containing reverberatedaudio signals can be generated. The reverberated audio signals can begenerated by adding reverberations of different types with differentstrengths to clean audio signals. In some examples, the de-noisetraining dataset 402, the de-reverberation training dataset 404, and thede-noise and de-reverberation training dataset 406 are stored in a datarepository accessible to the model training system 408. The modeltraining system 408 fully trains the de-noise teacher model 412 and thede-reverberation teacher model 414 using the de-noise training dataset402 and the de-reverberation training dataset 404, respectively.

The training of the de-noise and de-reverberation model 312 includes twostages. In the first stage, values generated by the de-noise teachermodel 412 and the de-reverberation teacher model 414 are utilized toguide the training of a portion of the parameters of the de-noise andde-reverberation model 312. In the example where the models are neuralnetwork models, the model training system 408 can retrieve the outputvalues of a hidden layer from each of the two teacher models 412 and414. The model training system 408 can further adjust the parameters ofthe de-noise and de-reverberation model 312 in the input layer and thehidden layers to minimize a loss function defined based on the hiddenlayer output values of the two teacher models 412 and 414.

The second stage of the training is performed independently of the twoteacher models. In this stage, the parameters of the entire de-noise andde-reverberation model 312 are adjusted or updated to minimize a lossfunction defined based on the similarity between the cleaned audiosignals generated by the de-noise and de-reverberation model 312 and theground truth clean audio signal in the training dataset. The trainingdataset is the de-noise and de-reverberation training dataset 406generated for the de-noise and de-reverberation model 312. The trainedde-noise and de-reverberation model 312 can be provided to clientdevices of the videoconferencing system to simultaneously remove noiseand reverberation from recorded audio signals as described above.Additional details about training the de-noise and de-reverberationmodel 312 are provided below with respect to FIGS. 5 and 6 .

Referring now to FIG. 5 , FIG. 5 includes a flow chart that illustratesa process 500 for training a de-noise and de-reverberation model,according to some aspects described herein. FIG. 5 will be described inconjunction with FIG. 6 which shows an example of the parameters of themodels involved in the training of the de-noise and de-reverberationmodel. FIG. 5 will be described with respect to the system shown in FIG.4 . However, any suitable system according to this disclosure may beemployed. The model training system 408 or another computing system canimplement the operations in the process 500.

At block 502, the process 500 involves generating training datasets forthe models involved in the training of the de-noise and de-reverberationmodel 312. The model training system 408 or another computing device cangenerate the de-noise training dataset 402, the de-reverberationtraining dataset 404, and the de-noise and de-reverberation trainingdataset 406 and store them in the data repository 410. To generate thede-noise training dataset 402, the model training system 408 or anothercomputing device can access various noise signals that are simulated orrecorded in real environments. These noises can be added to a cleanaudio signal to generate noisy audio signals as samples in the de-noisetraining dataset 402. In some examples, the strength of the noise can bechanged to different values to generate noisy signals with differentsignal-to-noise ratios (SNRs). The clean audio signals can serve as theground truth signals in the de-noise training dataset 402.

To generate the de-reverberation training dataset 404, the modeltraining system 408 or another computing device can use a reverberationtool to simulate the reverberation according to various reverberationparameters. The reverberation parameters can include a pre-delay timethat describes the collection of reflected sound from direct sound to 50ms, a room-scale parameter describing the size of the space where thereverberation effect takes place, a volume of the reverberation which isrelated to the number of items in the room and the materials of thewalls or items in the room, and other parameters. These reverberationsgenerated by the tool can be added to a clean audio signal to generatereverberated audio signals as samples in the de-reverberation trainingdataset 404. Similar to the noisy audio signals, the strength of thereverberation can be changed to different values to generatereverberated signals with different reverberation parameters. The cleanaudio signals can serve as the ground truth signals in thede-reverberation training dataset 404.

The de-noise and de-reverberation training dataset 406 can beconstructed by including a portion or all of samples from the de-noisetraining dataset 402 and a portion or all of samples from thede-reverberation training dataset 404. In some examples, the de-noiseand de-reverberation training dataset 406 can also be constructed byadding recorded or simulated noises and reverberations to clean audiosignals. In other words, a sample in the de-noise and de-reverberationtraining dataset 406 can be constructed by adding a noise component anda reverberation component to a clean audio signal and use the cleansignal as the ground truth signals. The noise component and thereverberation component can be generated in a way similar to generatingthe noise for the de-noise training dataset 402 and the reverberationfor the de-reverberation training dataset 404. In some examples, thesetwo types of training datasets are both used in the training of thede-noise and de-reverberation model 312. For instance, the first type oftraining dataset is used during the first stage of training the de-noiseand de-reverberation model 312 and the second type of training datasetis used in the second stage of training of the de-noise andde-reverberation model 312.

At block 504, the process 500 involves constructing the de-noise teachermodel 412, the de-reverberation teacher model 414, and the de-noise andde-reverberation model 312. As discussed above with respect to FIG. 4 ,the de-noise teacher model 412 and the de-reverberation teacher model414 can each be constructed to be a full-fledged model with a morecomplex model structure than the de-noise and de-reverberation model312. The structures or scales of the de-noise teacher model 412 and thede-reverberation teacher model 414 may be the same or different.

At block 506, the process 500 involves training the de-noise teachermodel 412 using the de-noise training dataset 402 and training thede-reverberation teacher model 414 using the de-reverberation trainingdataset 404. In the above examples where the models are neural networkmodels, the de-noise teacher model 412 and the de-reverberation teachermodel 414 can each be trained using the back-propagation algorithm.During the training, the input signal to each of the de-noise teachermodel 412 and the de-reverberation teacher model 414 can be generated bydividing the input audio signals into segments and applying atransformation to each segment. The input signals to each of the modelsare thus transformed audio signals. The transformation can be ashort-time Fourier transform (STFT). In some examples, The transformedsignal may further be processed, such as by applying a log function, toobtain the final input signals to the model. The input to the de-noiseand de-reverberation model 312 can be similarly constructed.

At block 506, the process 500 involves the first stage of training thede-noise and de-reverberation model 312. In the first stage, the modeltraining system 408 determines a portion of the parameters of thede-noise and de-reverberation model 312 based on the trained de-noiseteacher model 412 and the trained de-reverberation teacher model 414. Insome examples, the model training system 408 can retrieve the outputvalues of a hidden layer from each of the two teacher models and furtheradjust the parameters of the de-noise and de-reverberation model in theinput layer and the hidden layers to minimize a loss function definedbased on the output values of the two teacher models.

FIG. 6 shows an example of the parameters of the models involved duringthe first stage training of the de-noise and de-reverberation model,according to certain aspects described herein. Assume that the de-noiseteacher model 412 has L layers with the input layer as the first layer,the output layer as the L-th layer, and hidden layers being the secondlayer to the (L−1)-th layer. In this example, the output of the lasthidden layer (i.e., layer L−1) of the de-noise teacher model 412 is usedto guide the training of the de-noise and de-reverberation model, andthus the last hidden layer is referred to as a guided layer. Theparameters (e.g., weights of connections between nodes) for the i-thlayer of the de-noise teacher model 412 are denoted as W_(N) ^(i). Thecollection of the parameters of all the layers of the de-noise teachermodel 412 is denoted by W_(N). The parameters of the first L−1 layers(from the first layer up to the guided layer) are denoted as W_(N)^(hint).

Similarly, assume that the de-reverberation teacher model 414 has Mlayers and the parameters of the model are denoted by W_(R). Theparameters (e.g., weights of connections between nodes) for the i-thlayer of the de-reverberation teacher model 414 are denoted as W_(R)^(i). The last hidden layer (i.e., (M−1)-th layer) is selected as theguided layer and the parameters of the first M−1 layers are denoted asW_(R) ^(hint). For the de-noise and de-reverberation model 312, assumethe model has N layers, and the parameters (e.g., weights of connectionsbetween nodes) for the i-th layer of the de-noise and de-reverberationmodel 312 are denoted as W_(S) ^(i). The parameters of all the layers ofthe model are denoted by W_(S). The parameters of the first N−1 layersare denoted as W_(S) ^(guided). As used herein, a hint refers to theoutput of a hidden layer in a teacher model responsible for guiding thelearning process of the de-noise and de-reverberation model 312. In thisexample, the teacher model is the de-noise teacher model 412 or thede-reverberation teacher model 414.

In order to use the two teacher models to guide the training of thede-noise and de-reverberation model, the inputs to the three models arecoordinated during the training of the de-noise and de-reverberationmodel 312. In one example, the first type of de-noise andde-reverberation training dataset 406 is used for the training. Forexample, the samples in the de-noise training dataset 402 are providedto the de-noise teacher model 412 and the de-noise and de-reverberationmodel 312 as input. The samples in the de-reverberation training dataset404 are provided to the de-reverberation teacher model 414 and thede-noise and de-reverberation model 312 as input.

In this training stage, the training is performed by adjusting theparameters W_(S) ^(guided) of the de-noise and de-reverberation model312 to minimize a loss function

_(HT) as follows:

$\begin{matrix}{W_{S}^{{guided}*} = {\underset{W_{S}^{guided}}{argmin}{\mathcal{L}_{HT}( {W_{S}^{guided},\ W_{r}} )}}} & (1)\end{matrix}$ $\begin{matrix}{{\mathcal{L}_{TH}( {W_{s}^{guided},\ W_{r}} )} = {\frac{1}{2}{{{C( {( {{u_{N}^{hint}( {x_{N};W_{N}^{hint}} )} - {r( {{u_{s}^{guided}( {x_{N};W_{S}^{guided}} )};W_{r}} )}} )( {{u_{R}^{hint}( {x_{R};W_{R}^{hint}} )} - {r( {{u_{s}^{guided}( {x_{R};W_{S}^{guided}} )};W_{r}} )}} )} )}}^{2}.}}} & (2)\end{matrix}$

Here, x_(N) and x_(R) are input signals to the de-noise teacher model412 and the de-reverberation teacher model 414, respectively. The inputsignal x_(N) and x_(R) can be the transformed and processed audio signalas discussed above. C is a function that combines the outputs of thehint layers of the two teacher models. The function may be addition,weighted average, or other functions. u_(N) ^(hint)O and u_(R) ^(hint)Oare the deep-nested functions of the teacher models up to theirrespective hint layers with parameters W_(N) ^(hint) and W_(R) ^(hint).u_(S) ^(guided)O is the deep-nested function of the de-noise andde-reverberation model up to the guided layer with parameters W_(S)^(guided). rO is a regression function added on top of the guided layerof the de-noise and de-reverberation model with parameters W_(r). The rOfunction is used to increase the dimension of the output of the lasthidden layer of the de-noise and de-reverberation model so that theoutput dimension matches that of the two teacher models. The parametersW_(r) are also trainable parameters.

Referring back to FIG. 5 , at block 510, the process 500 involves thesecond stage of the training of the de-noise and de-reverberation model312, that is, fine-tuning the parameters of the de-noise andde-reverberation model. In one example, the parameters to be fine-tunedor adjusted are parameters of all the layers of the de-noise andde-reverberation model 312, i.e., W_(S) in the example shown in FIG. 6 .This fine-tuning stage can be performed by changing W_(S) to minimize asecond stage loss function as follows:

$\begin{matrix}{W_{S}^{*} = {\underset{W_{S}}{argmin}{{\mathcal{L}( W_{S} )}.}}} & (3)\end{matrix}$

The loss function

can be a loss function measuring the difference between the outputsignal of the de-noise and de-reverberation model 312 and the groundtruth signal, such as the L₂ loss or a cosine similarity loss definedas:

$\begin{matrix}{{{cosine\_ similarity}{\_ loss}} = {- \frac{s \cdot t}{ {s} \middle| {t} }}} & (4)\end{matrix}$

Here, s and t represent the output of the model and the ground truthsignal, respectively. The training samples used for the second-stagetraining can include the samples in the second type of de-noise andde-reverberation training dataset 406 as discussed above.

At block 512, the process 500 involves outputting the trained de-noiseand de-reverberation model 312. As discussed above with respect to FIG.3 , the trained de-noise and de-reverberation model can be installed onclient devices 304 or the video conference provider 210 to clean up thecaptured audio signal before sending it to other participants of ameeting. In other examples, the trained de-noise and de-reverberationmodel can be used for other purposes, such as to clean up the audiosignal for speech recognition, voice recognition, or playing through anaudio output device like a speaker.

It should be understood that the operations shown in the process 500illustrated in FIG. 5 are for illustration purposes only and should notbe construed as limiting. More or fewer operations may be performed totrain the de-noise and de-reverberation model.

While the above description focuses on a de-noise and de-reverberationmodel for removing noise and reverberation simultaneously, the sametechniques can be utilized to generate and train a model for purposesother than noise and reverberation removal or purposes other than audiosignal processing. For example, the same techniques can be utilized togenerate a model for image processing, video processing, naturallanguage processing, or any tasks that have two goals to be achieved atthe same time.

In addition, the techniques can also be used to simultaneously achievemore than two goals. For example, a model can be built by employing morethan two teacher models with each of the teacher models configured toachieve one or more of the multiple goals. A hidden layer output fromeach of the teacher models can be used to guide the training of themodel in a way similar to those described above. The same techniques canalso be utilized to generate and train a model using a single teachermodel. The model can have any type of output, including continuoussignal output such as the audio signal output presented herein. Thesingle teacher model can be utilized to guide the first stage trainingof the model so that a less complicated model can be built with similarperformance to the teacher model.

Referring now to FIG. 7 , FIG. 7 shows an example computing device 700suitable for implementing aspects of the techniques and technologiesdescribed herein. The example computing device 700 includes a processor710 which is in communication with the memory 720 and other componentsof the computing device 700 using one or more communications buses 702.The processor 710 is configured to execute processor-executableinstructions stored in the memory 720 to execute the model trainingsystem 408 or a portion thereof according to this disclosure or toperform one or more methods for training the de-noise andde-reverberation model according to different examples, such as part orall of the example process 500 described above with respect to FIG. 5 .The computing device, in this example, also includes one or more userinput devices 750, such as a keyboard, mouse, touchscreen, video capturedevice, microphone, etc., to accept user input. The computing device 700also includes a display 740 to provide visual output to a user.

The computing device 700 also includes a communications interface 730.In some examples, the communications interface 730 may enablecommunications using one or more networks, including a local areanetwork (“LAN”); wide area network (“WAN”), such as the Internet;metropolitan area network (“MAN”); point-to-point or peer-to-peerconnection; etc. Communication with other devices may be accomplishedusing any suitable networking protocol. For example, one suitablenetworking protocol may include the Internet Protocol (“IP”),Transmission Control Protocol (“TCP”), User Datagram Protocol (“UDP”),or combinations thereof, such as TCP/IP or UDP/IP.

While some examples of methods and systems herein are described in termsof software executing on various machines, the methods and systems mayalso be implemented as specifically-configured hardware, such asfield-programmable gate array (FPGA) specifically to execute the variousmethods according to this disclosure. For example, examples can beimplemented in digital electronic circuitry, or in computer hardware,firmware, software, or in a combination thereof. In one example, adevice may include a processor or processors. The processor comprises acomputer-readable medium, such as a random access memory (RAM) coupledto the processor. The processor executes computer-executable programinstructions stored in memory, such as executing one or more computerprograms. Such processors may comprise a microprocessor, a digitalsignal processor (DSP), an application-specific integrated circuit(ASIC), field programmable gate arrays (FPGAs), and state machines. Suchprocessors may further comprise programmable electronic devices such asPLCs, programmable interrupt controllers (PICs), programmable logicdevices (PLDs), programmable read-only memories (PROMs), electronicallyprogrammable read-only memories (EPROMs or EEPROMs), or other similardevices.

Such processors may comprise, or may be in communication with, media,for example one or more non-transitory computer-readable media, that maystore processor-executable instructions that, when executed by theprocessor, can cause the processor to perform methods according to thisdisclosure as carried out, or assisted, by a processor. Examples ofnon-transitory computer-readable medium may include, but are not limitedto, an electronic, optical, magnetic, or other storage device capable ofproviding a processor, such as the processor in a web server, withprocessor-executable instructions. Other examples of non-transitorycomputer-readable media include, but are not limited to, a floppy disk,CD-ROM, magnetic disk, memory chip, ROM, RAM, ASIC, configuredprocessor, all optical media, all magnetic tape or other magnetic media,or any other medium from which a computer processor can read. Theprocessor, and the processing, described may be in one or morestructures, and may be dispersed through one or more structures. Theprocessor may comprise code to carry out methods (or parts of methods)according to this disclosure.

Various examples are described for systems and methods for jointde-noise and de-reverberation of audio signals for videoconferences.

Clause 1: A computer-implemented method in which one or more processingdevices perform operations comprising: receiving an audio signalrecorded in a physical environment; applying a de-noise andde-reverberation model onto the audio signal to generate a cleaned audiosignal, wherein the de-noise and de-reverberation model is configured toremove noise and reverberation from the audio signal and is trained viaa training process comprising: generating a plurality of trainingdatasets that comprise a first training dataset for a de-noise teachermodel, a second training dataset for a de-reverberation teacher model,and a third training dataset for the de-noise and de-reverberationmodel; constructing the de-noise teacher model, the de-reverberationteacher model, and the de-noise and de-reverberation model; training thede-noise teacher model and the de-reverberation teacher model using thefirst training dataset and the second training dataset, respectively;training the de-noise and de-reverberation model by at least: adjustinga portion of parameters of the de-noise and de-reverberation model usingthe third training dataset and based on values generated by the de-noiseteacher model and the de-reverberation teacher model; and adjusting theparameters of the de-noise and de-reverberation model independently ofthe de-noise teacher model and the de-reverberation teacher model; andoutputting the cleaned audio signal.

Clause 2: The method of clause 1, wherein each of the de-noise teachermodel, the de-reverberation teacher model, and the de-noise andde-reverberation model is a neural network model, and each of thede-noise teacher model and the de-reverberation teacher model has alarger number of layers and a larger number of nodes than the de-noiseand de-reverberation model.

Clause 3: The method of clause 1 or 2, wherein: the first trainingdataset is generated by adding noise signals to a clean audio signal;the second training dataset is generated by adding reverberation signalsto the clean audio signal; and the third training dataset comprises atleast a portion of the first training dataset and at least a portion ofthe second training dataset.

Clause 4: The method of any of clauses 1-3, wherein the third trainingdataset is generated by adding reverberation signals and noise signalsto the clean audio signal.

Clause 5: The method of any of clauses 1-4, wherein each of the de-noiseteacher model, the de-reverberation teacher model, and the de-noise andde-reverberation model is a neural network model, and wherein adjustinga portion of parameters of the de-noise and de-reverberation modelcomprises: accessing a first output of a first hidden layer of thede-noise teacher model; accessing a second output of a second hiddenlayer of the de-reverberation teacher model; transforming a third outputof a third hidden layer of the de-noise and de-reverberation model tomatch a dimension of the first output and the second output; andadjusting the portion of parameters of the de-noise and de-reverberationmodel by minimizing a loss function calculated based on the firstoutput, second output, and the transformed third output, the portion ofparameters comprising weights for an input layer and hidden layers belowthe third hidden layer of the de-noise and de-reverberation model.

Clause 6: The method of any of clauses 1-5, wherein adjusting theparameters of the de-noise and de-reverberation model comprisesminimizing a loss function defined based on cleaned audio signalsgenerated by the de-noise and de-reverberation model for samplescontained in the third training dataset and ground truth clean signalsin the third training dataset.

Clause 7: The method of any of clauses 1-6, wherein the one or moreprocessing devices comprise at least one of a client device or a videoconference provider.

Clause 8: The method of any of clauses 1-7, wherein outputting thecleaned audio signal comprises one or more of: transmitting the cleanedaudio signal to a remote device; playing the cleaned audio signalthrough an audio output device; or sending the cleaned audio signal to acomponent configured to further process the cleaned audio signal.

Clause 9: A non-transitory computer-readable media communicativelycoupled to one or more processors and storing processor-executableinstructions that, when executed by the one or more processors, causethe one or more processors to perform operations comprising: receivingan audio signal recorded in a physical environment; applying a de-noiseand de-reverberation model onto the audio signal to generate a cleanedaudio signal, wherein the de-noise and de-reverberation model isconfigured to remove noise and reverberation from the audio signal andis trained via a training process comprising: generating a plurality oftraining datasets that comprise a first training dataset for a de-noiseteacher model, a second training dataset for a de-reverberation teachermodel, and a third training dataset for the de-noise andde-reverberation model; constructing the de-noise teacher model, thede-reverberation teacher model, and the de-noise and de-reverberationmodel; training the de-noise teacher model and the de-reverberationteacher model using the first training dataset and the second trainingdataset, respectively; training the de-noise and de-reverberation modelby at least: adjusting a portion of parameters of the de-noise andde-reverberation model using the third training dataset and based onvalues generated by the de-noise teacher model and the de-reverberationteacher model; and adjusting the parameters of the de-noise andde-reverberation model independently of the de-noise teacher model andthe de-reverberation teacher model; and outputting the cleaned audiosignal.

Clause 10: The non-transitory computer-readable media of clause 9,wherein each of the de-noise teacher model, the de-reverberation teachermodel, and the de-noise and de-reverberation model is a neural networkmodel, and each of the de-noise teacher model and the de-reverberationteacher model has a larger number of layers and a larger number of nodesthan the de-noise and de-reverberation model.

Clause 11: The non-transitory computer-readable media of clause 9 orclause 10, wherein: a sample in the first training dataset is a noisyaudio signal comprising a noise component and a clean audio signal; asample in the second training dataset is a reverberated audio signalcomprising a reverberation component and a clean audio signal; and asample in the third training dataset comprises a noise component, areverberation component and a clean audio signal.

Clause 12: The non-transitory computer-readable media of any of clauses9-11, wherein the third training dataset comprises a portion of thefirst training dataset and a portion of the second training dataset.

Clause 13: The non-transitory computer-readable media of any of clauses9-12, wherein each of the de-noise teacher model, the de-reverberationteacher model, and the de-noise and de-reverberation model is a neuralnetwork model, and wherein adjusting a portion of parameters of thede-noise and de-reverberation model comprises: accessing a first outputof a first hidden layer of the de-noise teacher model; accessing asecond output of a second hidden layer of the de-reverberation teachermodel; transforming a third output of a third hidden layer of thede-noise and de-reverberation model; and adjusting the portion ofparameters of the de-noise and de-reverberation model to minimize a lossfunction calculated based on the first output, second output, and thetransformed third output, the portion of parameters comprising weightsfor an input layer and hidden layers below the third hidden layer of thede-noise and de-reverberation model.

Clause 14: The non-transitory computer-readable media of any of clauses9-13, wherein adjusting the parameters of the de-noise andde-reverberation model comprises minimizing a loss function definedbased on cleaned audio signals generated by the de-noise andde-reverberation model for samples contained in the third trainingdataset and ground truth clean signals in the third training dataset.

Clause 15: The non-transitory computer-readable media of any of clauses9-14, wherein outputting the cleaned audio signal comprises one or moreof: transmitting the cleaned audio signal to a remote device; playingthe cleaned audio signal through an audio output device; or sending thecleaned audio signal to a component configured to further process thecleaned audio signal.

Clause 16: A system comprising: a processor; and a memory deviceincluding instructions that are executable by the processor to cause theprocessor to perform operations comprising: receiving an audio signalrecorded in a physical environment; applying a de-noise andde-reverberation model onto the audio signal to generate a cleaned audiosignal, wherein the de-noise and de-reverberation model is configured toremove noise and reverberation from the audio signal and is trained viaa training process comprising: generating a plurality of trainingdatasets that comprise a first training dataset for a de-noise teachermodel, a second training dataset for a de-reverberation teacher model,and a third training dataset for the de-noise and de-reverberationmodel; constructing the de-noise teacher model, the de-reverberationteacher model, and the de-noise and de-reverberation model; training thede-noise teacher model and the de-reverberation teacher model using thefirst training dataset and the second training dataset, respectively;training the de-noise and de-reverberation model by at least: adjustinga portion of parameters of the de-noise and de-reverberation model usingthe third training dataset and based on values generated by the de-noiseteacher model and the de-reverberation teacher model; and adjusting theparameters of the de-noise and de-reverberation model independently ofthe de-noise teacher model and the de-reverberation teacher model; andoutputting the cleaned audio signal.

Clause 17: The system of clause 16, wherein each of the de-noise teachermodel, the de-reverberation teacher model, and the de-noise andde-reverberation model is a neural network model, and each of thede-noise teacher model and the de-reverberation teacher model has alarger number of layers and a larger number of nodes than the de-noiseand de-reverberation model.

Clause 18: The system of clause 16 or clause 17, wherein each of thede-noise teacher model, the de-reverberation teacher model, and thede-noise and de-reverberation model is a neural network model, andwherein adjusting a portion of parameters of the de-noise andde-reverberation model comprises: accessing a first output of a firsthidden layer of the de-noise teacher model; accessing a second output ofa second hidden layer of the de-reverberation teacher model;transforming a third output of a third hidden layer of the de-noise andde-reverberation model to match a dimension of the first output and thesecond output; and adjusting the portion of parameters of the de-noiseand de-reverberation model by minimizing a loss function calculatedbased on the first output, second output, and the transformed thirdoutput, the portion of parameters comprising weights for an input layerand hidden layers below the third hidden layer of the de-noise andde-reverberation model.

Clause 19: The system of any of clauses 16-18, wherein adjusting theparameters of the de-noise and de-reverberation model comprisesminimizing a loss function defined based on cleaned audio signalsgenerated by the de-noise and de-reverberation model for samplescontained in the third training dataset and ground truth clean signalsin the third training dataset.

Clause 20: The system of any of clauses 16-19, wherein outputting thecleaned audio signal comprises one or more of: transmitting the cleanedaudio signal to a remote device; playing the cleaned audio signalthrough an audio output device; or sending the cleaned audio signal to acomponent configured to further process the cleaned audio signal.

The foregoing description of some examples has been presented only forthe purpose of illustration and description and is not intended to beexhaustive or to limit the disclosure to the precise forms disclosed.Numerous modifications and adaptations thereof will be apparent to thoseskilled in the art without departing from the spirit and scope of thedisclosure.

Reference herein to an example or implementation means that a particularfeature, structure, operation, or other characteristic described inconnection with the example may be included in at least oneimplementation of the disclosure. The disclosure is not restricted tothe particular examples or implementations described as such. Theappearance of the phrases “in one example,” “in an example,” “in oneimplementation,” or “in an implementation,” or variations of the same invarious places in the specification does not necessarily refer to thesame example or implementation. Any particular feature, structure,operation, or other characteristic described in this specification inrelation to one example or implementation may be combined with otherfeatures, structures, operations, or other characteristics described inrespect of any other example or implementation.

Use herein of the word “or” is intended to cover inclusive and exclusiveOR conditions. In other words, A or B or C includes any or all of thefollowing alternative combinations as appropriate for a particularusage: A alone; B alone; C alone; A and B only; A and C only; B and Conly; and A and B and C.

That which is claimed is:
 1. A computer-implemented method in which oneor more processing devices perform operations comprising: receiving anaudio signal recorded in a physical environment; applying a de-noise andde-reverberation model onto the audio signal to generate a cleaned audiosignal, wherein the de-noise and de-reverberation model is configured toremove noise and reverberation from the audio signal and is trained viaa training process comprising: generating a plurality of trainingdatasets that comprise a first training dataset for a de-noise teachermodel, a second training dataset for a de-reverberation teacher model,and a third training dataset for the de-noise and de-reverberationmodel; constructing the de-noise teacher model, the de-reverberationteacher model, and the de-noise and de-reverberation model; training thede-noise teacher model and the de-reverberation teacher model using thefirst training dataset and the second training dataset, respectively;training the de-noise and de-reverberation model by at least: adjustinga portion of parameters of the de-noise and de-reverberation model usingthe third training dataset and based on values generated by the de-noiseteacher model and the de-reverberation teacher model; and adjusting theparameters of the de-noise and de-reverberation model independently ofthe de-noise teacher model and the de-reverberation teacher model; andoutputting the cleaned audio signal.
 2. The method of claim 1, whereineach of the de-noise teacher model, the de-reverberation teacher model,and the de-noise and de-reverberation model is a neural network model,and each of the de-noise teacher model and the de-reverberation teachermodel has a larger number of layers and a larger number of nodes thanthe de-noise and de-reverberation model.
 3. The method of claim 1,wherein: the first training dataset is generated by adding noise signalsto a clean audio signal; the second training dataset is generated byadding reverberation signals to the clean audio signal; and the thirdtraining dataset comprises at least a portion of the first trainingdataset and at least a portion of the second training dataset.
 4. Themethod of claim 1, wherein the third training dataset is generated byadding reverberation signals and noise signals to the clean audiosignal.
 5. The method of claim 1, wherein each of the de-noise teachermodel, the de-reverberation teacher model, and the de-noise andde-reverberation model is a neural network model, and wherein adjustinga portion of parameters of the de-noise and de-reverberation modelcomprises: accessing a first output of a first hidden layer of thede-noise teacher model; accessing a second output of a second hiddenlayer of the de-reverberation teacher model; transforming a third outputof a third hidden layer of the de-noise and de-reverberation model tomatch a dimension of the first output and the second output; andadjusting the portion of parameters of the de-noise and de-reverberationmodel by minimizing a loss function calculated based on the firstoutput, second output, and the transformed third output, the portion ofparameters comprising weights for an input layer and hidden layers belowthe third hidden layer of the de-noise and de-reverberation model. 6.The method of claim 1, wherein adjusting the parameters of the de-noiseand de-reverberation model comprises minimizing a loss function definedbased on cleaned audio signals generated by the de-noise andde-reverberation model for samples contained in the third trainingdataset and ground truth clean signals in the third training dataset. 7.The method of claim 1, wherein the one or more processing devicescomprise at least one of a client device or a video conference provider.8. The method of claim 1, wherein outputting the cleaned audio signalcomprises one or more of: transmitting the cleaned audio signal to aremote device; playing the cleaned audio signal through an audio outputdevice; or sending the cleaned audio signal to a component configured tofurther process the cleaned audio signal.
 9. A non-transitorycomputer-readable media communicatively coupled to one or moreprocessors and storing processor-executable instructions that, whenexecuted by the one or more processors, cause the one or more processorsto perform operations comprising: receiving an audio signal recorded ina physical environment; applying a de-noise and de-reverberation modelonto the audio signal to generate a cleaned audio signal, wherein thede-noise and de-reverberation model is configured to remove noise andreverberation from the audio signal and is trained via a trainingprocess comprising: generating a plurality of training datasets thatcomprise a first training dataset for a de-noise teacher model, a secondtraining dataset for a de-reverberation teacher model, and a thirdtraining dataset for the de-noise and de-reverberation model;constructing the de-noise teacher model, the de-reverberation teachermodel, and the de-noise and de-reverberation model; training thede-noise teacher model and the de-reverberation teacher model using thefirst training dataset and the second training dataset, respectively;training the de-noise and de-reverberation model by at least: adjustinga portion of parameters of the de-noise and de-reverberation model usingthe third training dataset and based on values generated by the de-noiseteacher model and the de-reverberation teacher model; and adjusting theparameters of the de-noise and de-reverberation model independently ofthe de-noise teacher model and the de-reverberation teacher model; andoutputting the cleaned audio signal.
 10. The non-transitorycomputer-readable media of claim 9, wherein each of the de-noise teachermodel, the de-reverberation teacher model, and the de-noise andde-reverberation model is a neural network model, and each of thede-noise teacher model and the de-reverberation teacher model has alarger number of layers and a larger number of nodes than the de-noiseand de-reverberation model.
 11. The non-transitory computer-readablemedia of claim 9, wherein: a sample in the first training dataset is anoisy audio signal comprising a noise component and a clean audiosignal; a sample in the second training dataset is a reverberated audiosignal comprising a reverberation component and a clean audio signal;and a sample in the third training dataset comprises a noise component,a reverberation component and a clean audio signal.
 12. Thenon-transitory computer-readable media of claim 9, wherein the thirdtraining dataset comprises a portion of the first training dataset and aportion of the second training dataset.
 13. The non-transitorycomputer-readable media of claim 9, wherein each of the de-noise teachermodel, the de-reverberation teacher model, and the de-noise andde-reverberation model is a neural network model, and wherein adjustinga portion of parameters of the de-noise and de-reverberation modelcomprises: accessing a first output of a first hidden layer of thede-noise teacher model; accessing a second output of a second hiddenlayer of the de-reverberation teacher model; transforming a third outputof a third hidden layer of the de-noise and de-reverberation model; andadjusting the portion of parameters of the de-noise and de-reverberationmodel to minimize a loss function calculated based on the first output,second output, and the transformed third output, the portion ofparameters comprising weights for an input layer and hidden layers belowthe third hidden layer of the de-noise and de-reverberation model. 14.The non-transitory computer-readable media of claim 9, wherein adjustingthe parameters of the de-noise and de-reverberation model comprisesminimizing a loss function defined based on cleaned audio signalsgenerated by the de-noise and de-reverberation model for samplescontained in the third training dataset and ground truth clean signalsin the third training dataset.
 15. The non-transitory computer-readablemedia of claim 9, wherein outputting the cleaned audio signal comprisesone or more of: transmitting the cleaned audio signal to a remotedevice; playing the cleaned audio signal through an audio output device;or sending the cleaned audio signal to a component configured to furtherprocess the cleaned audio signal.
 16. A system comprising: a processor;and a memory device including instructions that are executable by theprocessor to cause the processor to perform operations comprising:receiving an audio signal recorded in a physical environment; applying ade-noise and de-reverberation model onto the audio signal to generate acleaned audio signal, wherein the de-noise and de-reverberation model isconfigured to remove noise and reverberation from the audio signal andis trained via a training process comprising: generating a plurality oftraining datasets that comprise a first training dataset for a de-noiseteacher model, a second training dataset for a de-reverberation teachermodel, and a third training dataset for the de-noise andde-reverberation model; constructing the de-noise teacher model, thede-reverberation teacher model, and the de-noise and de-reverberationmodel; training the de-noise teacher model and the de-reverberationteacher model using the first training dataset and the second trainingdataset, respectively; training the de-noise and de-reverberation modelby at least: adjusting a portion of parameters of the de-noise andde-reverberation model using the third training dataset and based onvalues generated by the de-noise teacher model and the de-reverberationteacher model; and adjusting the parameters of the de-noise andde-reverberation model independently of the de-noise teacher model andthe de-reverberation teacher model; and outputting the cleaned audiosignal.
 17. The system of claim 16, wherein each of the de-noise teachermodel, the de-reverberation teacher model, and the de-noise andde-reverberation model is a neural network model, and each of thede-noise teacher model and the de-reverberation teacher model has alarger number of layers and a larger number of nodes than the de-noiseand de-reverberation model.
 18. The system of claim 16, wherein each ofthe de-noise teacher model, the de-reverberation teacher model, and thede-noise and de-reverberation model is a neural network model, andwherein adjusting a portion of parameters of the de-noise andde-reverberation model comprises: accessing a first output of a firsthidden layer of the de-noise teacher model; accessing a second output ofa second hidden layer of the de-reverberation teacher model;transforming a third output of a third hidden layer of the de-noise andde-reverberation model to match a dimension of the first output and thesecond output; and adjusting the portion of parameters of the de-noiseand de-reverberation model by minimizing a loss function calculatedbased on the first output, second output, and the transformed thirdoutput, the portion of parameters comprising weights for an input layerand hidden layers below the third hidden layer of the de-noise andde-reverberation model.
 19. The system of claim 16, wherein adjustingthe parameters of the de-noise and de-reverberation model comprisesminimizing a loss function defined based on cleaned audio signalsgenerated by the de-noise and de-reverberation model for samplescontained in the third training dataset and ground truth clean signalsin the third training dataset.
 20. The system of claim 16, whereinoutputting the cleaned audio signal comprises one or more of:transmitting the cleaned audio signal to a remote device; playing thecleaned audio signal through an audio output device; or sending thecleaned audio signal to a component configured to further process thecleaned audio signal.